Smart Consumer Wearables as Digital Diagnostic Tools: A Review DOI Creative Commons

Shweta Chakrabarti,

Nupur Biswas, L. Jones

и другие.

Diagnostics, Год журнала: 2022, Номер 12(9), С. 2110 - 2110

Опубликована: Авг. 31, 2022

The increasing usage of smart wearable devices has made an impact not only on the lifestyle users, but also biological research and personalized healthcare services. These devices, which carry different types sensors, have emerged as digital diagnostic tools. Data from such enabled prediction detection various physiological well psychological conditions diseases. In this review, we focused applications wrist-worn wearables to detect multiple diseases cardiovascular diseases, neurological disorders, fatty liver metabolic including diabetes, sleep quality, illnesses. fruitful requires fast insightful data analysis, is feasible through machine learning. discussed machine-learning outcomes for analyses. Finally, current challenges with data, future perspectives tools domains.

Язык: Английский

Integration of Artificial Intelligence, Blockchain, and Wearable Technology for Chronic Disease Management: A New Paradigm in Smart Healthcare DOI Open Access
Yi Xie, Lin Lu, Fei Gao

и другие.

Current Medical Science, Год журнала: 2021, Номер 41(6), С. 1123 - 1133

Опубликована: Дек. 1, 2021

Язык: Английский

Процитировано

140

Challenges and recommendations for wearable devices in digital health: Data quality, interoperability, health equity, fairness DOI Creative Commons
Stefano Canali, Viola Schiaffonati, Andréa Aliverti

и другие.

PLOS Digital Health, Год журнала: 2022, Номер 1(10), С. e0000104 - e0000104

Опубликована: Окт. 13, 2022

Wearable devices are increasingly present in the health context, as tools for biomedical research and clinical care. In this wearables considered key a more digital, personalised, preventive medicine. At same time, have also been associated with issues risks, such those connected to privacy data sharing. Yet, discussions literature mostly focused on either technical or ethical considerations, framing these largely separate areas of discussion, contribution collection, development, application knowledge has only partially discussed. To fill gaps, article we provide an epistemic (knowledge-related) overview main functions wearable technology health: monitoring, screening, detection, prediction. On basis, identify 4 concern functions: quality, balanced estimations, equity, fairness. move field forward effective beneficial direction, recommendations areas: local standards interoperability, access, representativity.

Язык: Английский

Процитировано

128

The Influence of Wearables on Health Care Outcomes in Chronic Disease: Systematic Review DOI Creative Commons
Graeme Mattison, Oliver J. Canfell, Douglas L. Forrester

и другие.

Journal of Medical Internet Research, Год журнала: 2022, Номер 24(7), С. e36690 - e36690

Опубликована: Май 16, 2022

Chronic diseases contribute to high rates of disability and mortality. Patient engagement in chronic disease self-management is an essential component models health care. Wearables provide patient-centered data real time, which can help inform decision-making. Despite the perceived benefits wearables improving self-management, their influence on care outcomes remains poorly understood.This review aimed examine individuals with through a systematic literature.A narrative was conducted by searching 6 databases for randomized observational studies published between January 1, 2016, July 2021, that included use wearable intervention group assess its impact predefined outcome measure. These were defined as any patient or clinician experience, cost-effectiveness, result intervention. Data from extracted based key themes, formed basis qualitative synthesis. All mapped against each Quadruple Aim The guidelines PRISMA (Preferred Reporting Items Systematic Reviews Meta-Analyses) statement followed this study.A total 30 articles included; reported 2446 participants (mean age: range 10.1-74.4 years), 14 types 18 presented. most studied type 2 diabetes (4/30, 13%), Parkinson (3/30, 10%), lower back pain 10%). results mixed when assessing primary outcome, 50% (15/30) finding positive demonstrating nil effect. There effect 3D virtual reality systems 7% (2/30) evaluated distinct syndromes. Mixed observed influencing exercise capacity; weight; biomarkers disease, such hemoglobin A1c, diabetes. In total, 155 studied. Most (139/155, 89.7%) addressed component. This (11/155, 7.5%), quality life (7/155, 4.8%), physical function (5/155, 3.4%). Approximately 7.7% (12/155) measures represented experience component, 1.3% (2/155) addressing cost.Given popularity capability, may play integral role management. However, further research required generate strong evidence base safe effective implementation.PROSPERO International Prospective Register CRD42021244562; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=244562.

Язык: Английский

Процитировано

96

Use of Wearable Devices in Individuals With or at Risk for Cardiovascular Disease in the US, 2019 to 2020 DOI Creative Commons
Lovedeep Singh Dhingra, Arya Aminorroaya, Evangelos K. Oikonomou

и другие.

JAMA Network Open, Год журнала: 2023, Номер 6(6), С. e2316634 - e2316634

Опубликована: Июнь 7, 2023

Importance Wearable devices may be able to improve cardiovascular health, but the current adoption of these could skewed in ways that exacerbate disparities. Objective To assess sociodemographic patterns use wearable among adults with or at risk for disease (CVD) US population 2019 2020. Design, Setting, and Participants This population-based cross-sectional study included a nationally representative sample from Health Information National Trends Survey (HINTS). Data were analyzed June 1 November 15, 2022. Exposures Self-reported CVD (history heart attack, angina, congestive failure) factors (≥1 factor hypertension, diabetes, obesity, cigarette smoking). Main Outcomes Measures access devices, frequency use, willingness share health data clinicians (referred as care providers survey). Results Of overall 9303 HINTS participants representing 247.3 million (mean [SD] age, 48.8 [17.9] years; 51% [95% CI, 49%-53%] women), 933 (10.0%) 20.3 had 62.2 [17.0] 43% 37%-49%] 5185 (55.7%) 134.9 51.4 [16.9] women). In weighted assessments, an estimated 3.6 (18% 14%-23%]) 34.5 (26% 24%-28%]) used compared 29% (95% 27%-30%) adult population. After accounting differences demographic characteristics, profile, socioeconomic features, older age (odds ratio [OR], 0.35 0.26-0.48]), lower educational attainment (OR, 0.24-0.52]), household income 0.42 0.29-0.60]) independently associated CVD. Among device users, smaller proportion reported using every day (38% 26%-50%]) (49% 45%-53%]) at-risk (48% 43%-53%]) populations. 83% 70%-92%) 81% 76%-85%) favored sharing their care. Conclusions Relevance individuals CVD, fewer than 4 only half those reporting consistent daily use. As emerge tools can disparities unless there are strategies ensure equitable adoption.

Язык: Английский

Процитировано

79

Integrating Artificial Intelligence and Wearable IoT System in Long-Term Care Environments DOI Creative Commons
Wei‐Hsun Wang, Wen-Shin Hsu

Sensors, Год журнала: 2023, Номер 23(13), С. 5913 - 5913

Опубликована: Июнь 26, 2023

With the rapid advancement of information and communication technology (ICT), big data, artificial intelligence (AI), intelligent healthcare systems have emerged, including integration with capital, introduction into long-term care institutions, measurement data for or exposure. These provide comprehensive home exposure reports enable involvement rehabilitation specialists other experts. Silver enables realization health management in services, workplace care, applications, facilitating disease prevention control, improving management, reducing isolation, alleviating family burden terms nursing, promoting control. Research development efforts forward-looking cross-domain precision technology, system construction, testing, are carried out. This integrated project consists two main components. The Integrated Intelligent Long-Term Care Service Management System focuses on building a personalized service elderly, encompassing health, nutrition, diet, education aspects. Wearable Internet Things primarily supports portable physiological signal detection devices electronic fences.

Язык: Английский

Процитировано

64

AI-Enabled Wearable Medical Internet of Things in Healthcare System: A Survey DOI Creative Commons
Fazli Subhan, Alina Mirza,

Mazliham Bin Mohd Su’ud

и другие.

Applied Sciences, Год журнала: 2023, Номер 13(3), С. 1394 - 1394

Опубликована: Янв. 20, 2023

Technology has played a vital part in improving quality of life, especially healthcare. Artificial intelligence (AI) and the Internet Things (IoT) are extensively employed to link accessible medical resources deliver dependable effective intelligent Body wearable devices have garnered attention as powerful for healthcare applications, leading various commercially available multiple purposes, including individual healthcare, activity alerts, fitness. The paper aims cover all advancements made Medical (IoMT) systems, which been scrutinized from perceptions their efficacy detecting, preventing, monitoring diseases latest issues also included, such COVID-19 monkeypox. This thoroughly discusses directions proposed by researchers improve through artificial intelligence. approaches adopted overall accuracy, efficiency, security system discussed detail. highlights constraints opportunities developing AI enabled IoT-based systems.

Язык: Английский

Процитировано

56

Advances in Printed Electronic Textiles DOI Creative Commons
Md Rashedul Islam, Shaila Afroj, Junyi Yin

и другие.

Advanced Science, Год журнала: 2023, Номер 11(6)

Опубликована: Ноя. 27, 2023

Abstract Electronic textiles (e‐textiles) have emerged as a revolutionary solution for personalized healthcare, enabling the continuous collection and communication of diverse physiological parameters when seamlessly integrated with human body. Among various methods employed to create wearable e‐textiles, printing offers unparalleled flexibility comfort, integrating wearables into garments. This has spurred growing research interest in printed due their vast design versatility, material options, fabrication techniques, wide‐ranging applications. Here, comprehensive overview crucial considerations fabricating e‐textiles is provided, encompassing selection conductive materials substrates, well essential pre‐ post‐treatments involved. Furthermore, techniques specific requirements are discussed, highlighting advantages limitations each method. Additionally, multitude applications made possible by explored, such integration sensors, supercapacitors, heated Finally, forward‐looking perspective discussing future prospects emerging trends realm e‐textiles. As advancements science, technologies, innovation continue unfold, transformative potential healthcare beyond poised revolutionize way technology interacts benefits.

Язык: Английский

Процитировано

52

Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review DOI Creative Commons
Luca Neri, Matt T. Oberdier, Kirsten C. J. van Abeelen

и другие.

Sensors, Год журнала: 2023, Номер 23(10), С. 4805 - 4805

Опубликована: Май 16, 2023

Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-risk health conditions such as cardiovascular diseases, sleep apnea, other conditions. Recently, to facilitate early identification diagnosis, efforts been made in research development new wearable devices make them smaller, more comfortable, accurate, increasingly compatible with artificial intelligence technologies. These can pave way longer continuous monitoring different biosignals, including real-time detection thus providing timely accurate predictions events that drastically improve healthcare management patients. Most recent reviews focus on a specific category disease, use 12-lead electrocardiograms, or technology. However, we present advances electrocardiogram signals acquired from publicly available databases analysis methods detect predict diseases. As expected, most focuses heart emerging areas, mental stress. From methodological point view, although traditional statistical machine learning are still widely used, observe an increasing advanced deep methods, specifically architectures handle complexity biosignal data. typically include convolutional recurrent neural networks. Moreover, when proposing prevalent choice is rather than collecting

Язык: Английский

Процитировано

51

Hybrid multimodal wearable sensors for comprehensive health monitoring DOI
Kuldeep Mahato, Tamoghna Saha, Shichao Ding

и другие.

Nature Electronics, Год журнала: 2024, Номер unknown

Опубликована: Сен. 23, 2024

Язык: Английский

Процитировано

46

The hospital at home in the USA: current status and future prospects DOI Creative Commons
Jay Pandit, Jeff Pawelek, Bruce Leff

и другие.

npj Digital Medicine, Год журнала: 2024, Номер 7(1)

Опубликована: Фев. 27, 2024

Abstract The annual cost of hospital care services in the US has risen to over $1 trillion despite relatively worse health outcomes compared similar nations. These trends accentuate a growing need for innovative delivery models that reduce costs and improve outcomes. HaH—a program provides patients acute-level at home—has made significant progress past two decades. Technological advancements remote patient monitoring, wearable sensors, information technology infrastructure, multimodal data processing have contributed its rise across hospitals. More recently, COVID-19 pandemic brought HaH into mainstream, especially US, with reimbursement waivers model financially acceptable hospitals payors. However, continues face serious challenges gain widespread adoption. In this review, we evaluate peer-reviewed evidence discuss promises, challenges, what it would take tap future potential HaH.

Язык: Английский

Процитировано

33